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from typing import List, Dict
import httpx
import gradio as gr
import pandas as pd
from huggingface_hub import HfApi, ModelCard
def search_hub(query: str, search_type: str) -> pd.DataFrame:
api = HfApi()
if search_type == "Models":
results = api.list_models(search=query)
data = [{"id": model.modelId, "author": model.author, "downloads": model.downloads} for model in results]
elif search_type == "Datasets":
results = api.list_datasets(search=query)
data = [{"id": dataset.id, "author": dataset.author, "downloads": dataset.downloads} for dataset in results]
elif search_type == "Spaces":
results = api.list_spaces(search=query)
data = [{"id": space.id, "author": space.author} for space in results]
else:
data = []
return pd.DataFrame(data)
def open_url(row):
if row is not None and not row.empty:
url = f"https://huggingface.co/{row.iloc[0]['id']}"
return f'<a href="{url}" target="_blank">{url}</a>'
else:
return ""
def load_metadata(row, search_type):
if row is not None and not row.empty:
item_id = row.iloc[0]['id']
if search_type == "Models":
try:
card = ModelCard.load(item_id)
return card
except Exception as e:
return f"Error loading model card: {str(e)}"
elif search_type == "Datasets":
api = HfApi()
metadata = api.dataset_info(item_id)
return str(metadata)
elif search_type == "Spaces":
api = HfApi()
metadata = api.space_info(item_id)
return str(metadata)
else:
return ""
else:
return ""
with gr.Blocks() as demo:
gr.Markdown("## Search the Hugging Face Hub")
with gr.Row():
search_query = gr.Textbox(label="Search Query")
search_type = gr.Radio(["Models", "Datasets", "Spaces"], label="Search Type", value="Models")
search_button = gr.Button("Search")
results_df = gr.DataFrame(label="Search Results", wrap=True, interactive=True)
url_output = gr.HTML(label="URL")
metadata_output = gr.Textbox(label="Metadata", lines=10)
search_button.click(search_hub, inputs=[search_query, search_type], outputs=[results_df])
results_df.select(open_url, outputs=[url_output])
results_df.select(load_metadata, inputs=[results_df, search_type], outputs=[metadata_output])
demo.launch(debug=True) |